European countries, their dietary habits and diabetes prevalence

Introduction

Overview and Motivation

During the last 2 years, COVID-19 has been a main focus of the news. Though around 3% of the world population had COVID-19, diabetes can be considered as an even bigger health problem. Indeed, according to the International Diabetes Foundations (IDF), in 2019, 463 million adults were living with diabetes (around 6-7% of the world population) and this number is forecasted to rise to 700 million by 2050. Furthermore, 90% of cases of diabetes are of type 2, which means it results mainly from bad habits and not genetics. However both types of diabetes can be treated and/or prevented with a healthier diet and more physical activity. Additionally, according to the WHO, low income countries are more susceptible to having higher diabetes prevalence. Living in Europe, we observed that diabetes rates differ a lot from one country to another, so we wanted to find out if these rates were indeed linked to a country’s income, and if the nutritious composition of richer states’ population’s diet is also affected by this income difference and if yes, how it is affected.

Related Work

The article published by M. Adeva-Andany (2019) “Dietary habits contribute to define the risk of type 2 diabetes in humans” states that type 2 diabetes could be prevented by better dietary habits.They also found that higher animal protein consumption induced a greater risk of developing type 2 diabetes regardless of body mass index. In addition, they have also said that: “Low intake of animal products is linked to high educational level so that well-informed individuals tend to consume diets with elevated content of vegetable food” which can be linked to the GDP. (https://www.sciencedirect.com/science/article/pii/S240545771930419X?casa_token=Dzx3S9e8WgsAAAAA:IUYCy_uePTCOLUn38QUDO5KtpJopGpHdNIXZtHVVsGy9NWuXQ78Hp2juTvtjFQp67Dk2by-fMgPfrg)

Research questions

Therefore, we would like to find out answers to the following questions :

1. Do European countries that have higher GDPs really have lower diabetes prevalence ?

2. Do European countries that have higher GDPs consume less calories ?

3. How do the proportions of macronutrients (animal protein/plant protein/fat/carbohydrates) consumed differ between richer and poorer governments ?

4. And how do these differences relate to the diabetes prevalence in these countries ?

5. What is the typical diet that can be observed in richer states that relates to lower diabetes prevalence ?

Data

To answer our research questions, we used three different datasets. While searching for datasets, we made sure that the years and countries matched for every one of them.

Wrangling and cleaning

Caloric consumption

The first dataset we used, downloaded from the portal https://ourworldindata.org/diet-compositions, contains information related to the supply of macronutrients in calories for different countries. We used data related to food supply rather than food consumption, as the latter is harder to find and generally, supply reflects the population’s demand and therefore its food consumption. The dataset gives us information on the average nutrition of different countries from 1961 to 2013 :

• It is composed of 8981 observations of 7 variables:

  • Entity Name of the country
    
  • Code ISO country code
    
  • YearYear of the observation
    
  • Calories from animal protein (FAO (2017)) The average per capita supply of calories derived from animal protein all measured in kilocalories per person per day
    
  • Calories from plant protein (FAO (2017)) The average per capita supply of calories derived from plant protein, all measured in kilocalories per person per day
    
  • Calories from fat (FAO (2017))The average per capita supply of calories derived from fat, all measured in kilocalories per person per day
    
  • Calories from carbohydrates (FAO (2017)) The average per capita supply of calories derived from carbohydrates, all measured in kilocalories per person per day

The intake of specific macronutrients (carbohydrates, protein and fats) are derived based on average food composition factors – these factors are derived and presented in the Food and Agriculture Organisation’s (FAO) Food Balance Sheet Handbook (https://www.fao.org/faostat/en/#data).

We will only focus on observations of European countries in the 2000s.

We used the ISO code as it is standardized worldwide and does not have the risk of having different names in different tables like the countries’ names.

Then, we proceeded to compute the mean of the consumption for each type of macronutrient in each country between the years 2000 and 2013, and the sum of total calories per person per day for each country in order to answer our second research question.

We then created a new table by adding the sum of total calories per person per day for each country in order to get a broader view with the total consumption of calories. To make sure that the joining of tables go smoothly, we also removed duplicates and the country name column.

Our assumption was that a county’s wealth may fluctuate over the course of 10 years (ex: a dip during the economic crisis of 2008) but an overall mean is sufficient to compare the different countries and their riches.

• We now have a dataframe with the following variables :

  • country_code ISO country code
    
  • cal_prot_animalThe mean of the calories from animal protein consumed per person in each country in the years 2000-2013
    
  • cal_prot_plant The mean of the calories from plant protein consumed per person in each country in the years 2000-2013
    
  • cal_carbsThe mean of the calories from carbohydrates consumed per person in each country in the years 2000-2013
    
  • cal_fat The mean of the calories from fat consumed per person in each country in the years 2000-2013
    
  • total_consumption The total calorie consumption per person based on the means of the consumption of each type of macronutrients in each countries in the years 2000-2013

The final table is the following:

Table 1: Caloric Consumption

Country Code	Calories from animal protein	Calories from plant protein	Calories from carbohydrates	Calories from fat	Total consumption
AUT	245	169	1833	1454	3702
BEL	238	158	1856	1467	3719
BGR	155	168	1606	846	2775
HRV	168	148	1691	940	2946
CYP	197	127	1291	1019	2633
CZE	218	153	1728	1155	3254
DNK	273	157	1746	1190	3366
EST	212	167	1967	842	3188
FIN	269	166	1623	1177	3234
FRA	293	161	1611	1480	3545
DEU	240	159	1785	1276	3460
GRC	250	204	1744	1338	3536
HUN	193	153	1560	1221	3126
IRL	279	174	1950	1187	3590
ITA	241	204	1776	1390	3612
LVA	203	154	1687	1044	3087
LTU	274	198	2055	858	3385
LUX	288	148	1752	1318	3507
MLT	237	204	1932	994	3367
NLD	292	136	1599	1195	3222
POL	204	197	1969	1035	3405
PRT	275	177	1824	1240	3516
ROU	200	220	2003	916	3340
SVK	140	150	1610	952	2853
SVN	230	168	1664	1067	3129
ESP	279	159	1481	1323	3243
SWE	285	143	1566	1137	3131
CHE	237	138	1660	1392	3426
GBR	232	178	1748	1256	3414

GDP

Our second dataset, downloaded from the portal https://data.worldbank.org, gives us information about the GDP of many countries over the course of 60 years (1960-2020).

• It is composed of 266 observations of 65 variables :

  • Country Name Name of the country
    
  • Country Code ISO country code
    
  • Indicator Name equal to “GDP in current US$” for every row
    
  • Indicator Code equal to “NY.GDP.MKTP.CD” for every row
    
  • And a variable for each year from 1960 to 2020

As we can see below, RStudio imported the Excel file as is, and so our column names found themselves at the 3rd row and therefore column names of columns 3 to 65 have been attributed numbers.

We decided to fix that and to filter out the years that is in our interest and that we have in common with other tables, so the years 2000-2013. We decided to get rid of the Indicator Name and Indicator Code variables as well since the values are the same for every row and they do not provide additional useful information.

Now, we want to filter out the European countries, just like in the first table :

In order to join tables easily, we transformed the columns corresponding to different years to a single “year” column, in order to have at each row of this dataset the GDP of a certain country at a certain year.

To make it easier to manipulate data, we decided to rename our variables for this table as well. We also made sure that the type of our numeric variable (GDP) was numeric and not character, like it was by default. In order to have graphs that are easy to read in the exploratory data analysis, we also decided to divide the avg_gdp column by a billion.

Lastly, we computed the average GDP for each country over the years 2000-2013 in order to be able to plot different variables together.

Table 2: GDP

Country Name	Country Code	Average GDP (in billion $)
Austria	AUT	3.36e+11
Belgium	BEL	4.07e+11
Bulgaria	BGR	3.74e+10
Croatia	HRV	4.81e+10
Cyprus	CYP	2.02e+10
Czech Republic	CZE	1.58e+11
Denmark	DNK	2.75e+11
Estonia	EST	1.65e+10
Finland	FIN	2.17e+11
France	FRA	2.28e+12
Germany	DEU	3.00e+12
Greece	GRC	2.46e+11
Hungary	HUN	1.11e+11
Ireland	IRL	2.03e+11
Italy	ITA	1.87e+12
Latvia	LVA	2.10e+10
Lithuania	LTU	3.08e+10
Luxembourg	LUX	4.28e+10
Malta	MLT	7.27e+09
Netherlands	NLD	7.22e+11
Poland	POL	3.65e+11
Portugal	PRT	2.00e+11
Romania	ROU	1.25e+11
Slovak Republic	SVK	7.08e+10
Slovenia	SVN	3.95e+10
Spain	ESP	1.18e+12
Sweden	SWE	4.26e+11
Switzerland	CHE	4.90e+11
United Kingdom	GBR	2.42e+12

We now have a dataframe with the following variables :

• country_name name of the country
  
• country_code ISO code of the country
  
• avg_gdp the average GDP of a country over the course of 2000-2013

Population

Since we will be comparing the GDP with the calories consumed per person, it could be useful to have the GDP per person for the analysis. This is why we will be importing this dataset from https://data.worldbank.org/indicator/SP.POP.TOTL which gives us information on the evolution of the population per country over 1960-2020.

As this dataset comes from the same source and is the same file type as GDP, we can do proceed with the same wrangling

#> Warning in summarize(., avg_population = mean(avg_population)): This
#> is a call to papeR::summarize. If you want to use the dplyr function
#> explizitly call dplyr::summarize() on your data.

Diabetes

The third dataset, downloaded from https://www.ncdrisc.org/data-downloads-diabetes.html, gives us information about the age-standardised diabetes prevalence for each country and gender from 1980 to 2014.

• It is composed of 14’000 observations for 7 variables :

  • Country/Region/World Name of the country
    
  • ISO ISO country code
    
  • Sex Gender for which the diabetes prevalence is measured in a certain country at a certain year
    
  • Year Year of observation (1980-2014)
  • Age-standardised diabetes prevalence Diabetes rate considering all ages
    
  • Lower 95% uncertainty interval Lower confidence interval limit for the diabetes rate
  • Upper 95% uncertainty interval Higher confidence interval limit for the diabetes rate

Like with the first 2 datasets, we filtered our data to keep only European countries and observations between the years 2000 and 2013.

We also decided not to use the 95% confidence interval variable.

Then, we separated our dataset into two subsets. One with data about men.

Another one with data about women.

We then changed the variable names of these 2 subsets to facilitate joining tables later on.

Finally we grouped observations by country to get the mean prevalence/rate of diabetes between 2000 and 2013 for each European country :

• For men :

Table 3: Diabetes men

Country Code	Diabetes rate
AUT	0.053
BEL	0.057
BGR	0.073
CHE	0.050
CYP	0.077
CZE	0.078
DEU	0.059
DNK	0.055
ESP	0.084
EST	0.071
FIN	0.066
FRA	0.071
GBR	0.063
GRC	0.069
HRV	0.071
HUN	0.080
IRL	0.069
ITA	0.065
LTU	0.078
LUX	0.068
LVA	0.071
MLT	0.088
NLD	0.052
POL	0.074
PRT	0.075
ROU	0.062
SVK	0.072
SVN	0.066
SWE	0.058

• For women :

Table 4: Diabetes women

Country Code	Diabetes rate
AUT	0.053
BEL	0.057
BGR	0.073
CHE	0.050
CYP	0.077
CZE	0.078
DEU	0.059
DNK	0.055
ESP	0.084
EST	0.071
FIN	0.066
FRA	0.071
GBR	0.063
GRC	0.069
HRV	0.071
HUN	0.080
IRL	0.069
ITA	0.065
LTU	0.078
LUX	0.068
LVA	0.071
MLT	0.088
NLD	0.052
POL	0.074
PRT	0.075
ROU	0.062
SVK	0.072
SVN	0.066
SWE	0.058

We now have 2 dataframes with the following variables :

• country_code ISO code of the country
  
• prop_men_diabetes or prop_women_diabetesthe average diabetes rate in a country in the 2000-2013 timeframe

Joining tables

For the last step of our tidying, we joined all four tables in one dataset with the country_code key :

Finale Dataset: GDP, diabetes and calories

Country Name	Country Code	Average GDP (in billion $)	Men Diabetes	Women Diabetes	Calories from animal protein	Calories from plant protein	Calories from carbohydrates	Calories from fat	Total consumption
Austria	AUT	3.36e+11	0.053	0.034	245	169	1833	1454	3702
Belgium	BEL	4.07e+11	0.057	0.039	238	158	1856	1467	3719
Bulgaria	BGR	3.74e+10	0.073	0.064	155	168	1606	846	2775
Croatia	HRV	4.81e+10	0.071	0.059	168	148	1691	940	2946
Cyprus	CYP	2.02e+10	0.077	0.056	197	127	1291	1019	2633
Czech Republic	CZE	1.58e+11	0.078	0.065	218	153	1728	1155	3254
Denmark	DNK	2.75e+11	0.055	0.035	273	157	1746	1190	3366
Estonia	EST	1.65e+10	0.071	0.064	212	167	1967	842	3188
Finland	FIN	2.17e+11	0.066	0.044	269	166	1623	1177	3234
France	FRA	2.28e+12	0.071	0.044	293	161	1611	1480	3545
Germany	DEU	3.00e+12	0.059	0.040	240	159	1785	1276	3460
Greece	GRC	2.46e+11	0.069	0.060	250	204	1744	1338	3536
Hungary	HUN	1.11e+11	0.080	0.063	193	153	1560	1221	3126
Ireland	IRL	2.03e+11	0.069	0.049	279	174	1950	1187	3590
Italy	ITA	1.87e+12	0.065	0.047	241	204	1776	1390	3612
Latvia	LVA	2.10e+10	0.071	0.065	203	154	1687	1044	3087
Lithuania	LTU	3.08e+10	0.078	0.069	274	198	2055	858	3385
Luxembourg	LUX	4.28e+10	0.068	0.039	288	148	1752	1318	3507
Malta	MLT	7.27e+09	0.088	0.066	237	204	1932	994	3367
Netherlands	NLD	7.22e+11	0.052	0.037	292	136	1599	1195	3222
Poland	POL	3.65e+11	0.074	0.066	204	197	1969	1035	3405
Portugal	PRT	2.00e+11	0.075	0.052	275	177	1824	1240	3516
Romania	ROU	1.25e+11	0.062	0.059	200	220	2003	916	3340
Slovak Republic	SVK	7.08e+10	0.072	0.059	140	150	1610	952	2853
Slovenia	SVN	3.95e+10	0.066	0.065	230	168	1664	1067	3129
Spain	ESP	1.18e+12	0.084	0.059	279	159	1481	1323	3243
Sweden	SWE	4.26e+11	0.058	0.040	285	143	1566	1137	3131
Switzerland	CHE	4.90e+11	0.050	0.030	237	138	1660	1392	3426
United Kingdom	GBR	2.42e+12	0.063	0.049	232	178	1748	1256	3414

Missing values

We did not have any NA values in our tables, we think this is due to the fact that we really spent time on gathering quality data that matched in terms of dates and countries.

Exploratory data analysis

First, even though we will be taking the means of the variables with which we are trying to answer our questions, it is interesting to observe their evolution in each country over time. We started with the GDP.

Evolution of GDP per country

We can see that the GDP of France, Germany, Italy, Spain and the United Kingdom had a significant increase between 2000 and 2008.

Plotting GDP against Diabetes (Men & Women)

Now let’s see if there is a relation between the GDP of a country and its diabetes prevalence. (men = blue, women = red)

We observe that apart of 5 outliers, our observations are mostly bunched up at the left of the graph. We decided to exclude these 5 observations, to see if we can observe a trend with the other countries. These outliers, as we can see on the graph before, are the countries that had a big increase of GDP in the time period of 2000-2013.

Without the outliers, we can see a bit more clearly. Indeed, it seems that the richer a country is, the lesser it has a high diabetes rate among its population.

Evolution of the consumption of macronutrients in calories per country

For the second table, we tried to see again if there was a trend in the consumption of different macro-nutrients in the 2000s for each country in our sample.

In the different countries, there is one difference that stands out and that seems to be related to wealth. Indeed, countries with a higher GDP like Austria consume on average more fat as can be seen on this graph:

Whereas, countries with a lower GDP like Bulgaria have a lower fat consumption, as seen below:

There do not seem to be any trends in the graphs above and diets seem rather stable in each country, which is why we will take the average consumption for each macro-nutrient for our analysis. We can however note that the 5 outliers mentioned before tend to have a higher fat consumption than the countries with a smaller GDP.

Plotting GDP against calories consumed

We then wanted to analyse the relation between a country’s GDP and its individual consumption of each macronutrient as well as its total calorie consumption to see if there’s a trend.(total calories = orange, fat = blue, carbohydrates = purple, animal protein = red, plant protein = green)

We see that the calorie consumption does not really change. We wanted a close up on the relation between the total calorie consumption with the GDP for each country to see if we can spot outliers again, so we created other plots.

We end up again with these 5 outliers that have a higher than average GDP so if we remove them, we obtain the following plots :

Now we can more easily state that there’s a trend. It appears that the higher a country’s GDP, the higher the total calories consumed, contrary to our hypothesis.

Evolution of Diabetes per country

Once again, we tried to see if the diabetes prevalence in each country changed over the years 2000-2013.

We saw right away that the prevalence of diabetes is higher for man than women across all countries (there are however two exceptions : in Romania between 2000 and 2003 and Slovenia between 2000 and 2006).

We observed three different scenarios for countries that we selected: A decrease of diabetes over time for:

• Belgium
  
• Denmark
  
• Finland

We take Belgium as an example :

A decrease over time for women but not for men for :

• Austria
  
• Malta
  
• Netherlands
  
• Germany
  
• Italy
  
• Spain
  
• Switzerland

We take Austria as an example :

In other European countries, the prevalence of diabetes is increasing (at different paces) over time.

We take Croatia as an example :

Plotting Diabetes against each type of macronutrient consumption

Finally, we want to plot the relation between the diabetes prevalence against the total calorie consumption as well as each type of macronutrient consumed.

We can see a negative trend for the total consumption, the calories from animal protein and the calories from fat. We can observe a positive trend against calories from plant protein. For protein from carbohydrates, we can see a slighty positive trend for women.

Plotting Diabetes against each type of macronutrient consumption (without outliers)

Now, since they affected our plots that included the GDP variable so much, we want to see if we have different trends when we remove our 5 outliers.

Without our 5 outliers, we observe not much change in the trend of each type of calories consumed apart for carbohydrates where the trend changes for men and become slightly positive.

Analysis

• Answers to the research questions
• Different methods considered
• Competing approaches
• Justifications

1.Do European countries that have higher GDPs really have lower diabetes prevalence ?

This first question serves more as a control, since we learned during our research prior to our project that countries with higher GDPs tend to have lower diabetes rates. Indeed, we can observe that in the EDA.

It is important to note that, when we try to fit a linear model on these variables and observe correlations over all observations, we see that these relationships are not significant at all.

#> [1] -0.369
#> [1] -0.236

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	1.89e+12	1.09e+12	1.74	0.093	.
GDP_diabetes_cal$prop_men_diabetes	-2.00e+13	1.58e+13	-1.26	0.217

However, once we exclude outliers, we see that the relationship is way more significant !

#> [1] -0.696
#> [1] -0.739

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	7.42e+11	1.24e+11	5.99	<0.001	***
GDP_diabetes_cal2$prop_women_diabetes	-1.03e+13	2.26e+12	-4.55	<0.001	***

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	1.15e+12	1.87e+11	6.12	<0.001	***
GDP_diabetes_cal2$prop_men_diabetes	-1.40e+13	2.73e+12	-5.15	<0.001	***

LOOK SPECIFICALLY INTO RICH COUNTRIES ?

2.Do European countries that have higher GDPs consume less calories ?

As mentioned in the first point, countries with a higher GDP have a lower diabetes rate which could potentially be explained by the consumption of fewer calories.

But is there a real correlation between these two variables ? Let’s check :

#> [1] 0.355

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	-3.02e+12	1.81e+12	-1.67	0.106
GDP_diabetes_cal$total_consumption	1.07e+09	5.46e+08	1.97	0.059	.

Neither the correlation between these two variables nor the linear regression is significant. However, it would be interesting to look further. To do this we can use the clustering method. To determine the number of clusters we use the elbow method. This method examines the percentage of variance explained as a function of the number of clusters. It is based on the idea that a number of clusters should be chosen such that the addition of another cluster does not allow for a better modeling of the data. The percentage of variance explained by the clusters is plotted against the number of clusters.

#> Warning: Setting row names on a tibble is deprecated.

We therefore see from the graph above that the optimal number of clusters is 2. The allocation of countries according to their cluster is therefore as follows:

#> Warning in lda.default(x, grouping, ...): variables are collinear
#>  [1] 2 2 2 2 2 2 2 2 2 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1

Country	Cluster
AUT	2
BEL	2
BGR	2
HRV	2
CYP	2
CZE	2
DNK	2
EST	2
FIN	2
FRA	1
DEU	1
GRC	2
HUN	2
IRL	2
ITA	1
LVA	2
LTU	2
LUX	2
MLT	2
NLD	2
POL	2
PRT	2
ROU	2
SVK	2
SVN	2
ESP	2
SWE	2
GBR	2
CHE	1

To get a better idea of the difference between the clusters, we will plot the means of each dimension in each group:

We can therefore see that diabetes is indeed lower in the first cluster which is the countries with the highest GPD. However, in terms of total calorie intake it is also the first cluster that consumes the most calories. One might therefore think that calorie consumption is not the main reason why high-GDP countries have lower diabetes rates. Nevertheless, we found it interesting to try to make 3 clusters to see the difference in total calorie consumption compared to the GDP rate.

	Cluster
AUT	2
BEL	1
BGR	2
HRV	2
CYP	2
CZE	2
DNK	2
EST	2
FIN	2
FRA	3
DEU	3
GRC	2
HUN	2
IRL	2
ITA	3
LVA	2
LTU	2
LUX	2
MLT	2
NLD	1
POL	2
PRT	2
ROU	2
SVK	2
SVN	2
ESP	1
SWE	1
GBR	1
CHE	3

We then plot those 3 clusters to see the differences:

#> Warning in lda.default(x, grouping, ...): variables are collinear
#>  [1] 3 1 3 3 3 3 3 3 3 2 2 3 3 3 2 3 3 3 3 1 3 3 3 3 3 1 1 1 2

x
3
1
3
3
3
3
3
3
3
2
2
3
3
3
2
3
3
3
3
1
3
3
3
3
3
1
1
1
2

The table shows that there is a better distribution of GDP, we will again make a graph to compare the differences between the clusters. Oddly enough, it seems that a higher consumption of animal protein could be related to the rate of diabetes, which would be counter-intuitive to M. Adeva-Andany’s (2019) article “Dietary habits contribute to define the risk of type 2 diabetes in humans”. We are going to investigate this very issue of calorie consumption patterns that could be related to low diabetes rates.

3.How do the macronutrients (animal protein/plant protein/fat/carbohydrates) consumed differ between richer and poorer countries ?

We observed during the EDA that richer countries seemed to consume more fat on average. Now we want to see if we can confirm this relationship, and observe if there isn’t a correlation between the average GDP of a country and the consumption of other macronutrients too.

#> [1] 0.272
#> [1] 0.0433
#> [1] -0.0828
#> [1] 0.503

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	-7.16e+11	8.61e+11	-0.832	0.413
GDP_diabetes_cal$cal_prot_animal	5.28e+09	3.59e+09	1.470	0.153

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	2.76e+11	1.14e+12	0.243	0.809
GDP_diabetes_cal$cal_prot_plant	1.52e+09	6.75e+09	0.225	0.823

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	1.21e+12	1.59e+12	0.764	0.452
GDP_diabetes_cal$cal_carbs	-3.93e+08	9.11e+08	-0.432	0.669

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	-1.94e+12	8.27e+11	-2.35	0.027
GDP_diabetes_cal$cal_fat	2.13e+09	7.03e+08	3.03	0.005	**

The relationship between the average GDP of a country and the calories consumed from fat per person is therefore not as significant as we may have thought. For other macronutriments, there seems to be no correlation at all.

Another way to answer this research question can be to see the relationship between the wealth of a country and the proportions of the total calories consumed dedicated to each macronutrient.

#> [1] 0.147
#> [1] -0.186
#> [1] -0.437
#> [1] 0.428

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	-2.73e+11	1.05e+12	-0.260	0.797
GDP_diabetes_cal$proportion_animal_prot	1.13e+13	1.46e+13	0.771	0.447

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	1.74e+12	1.24e+12	1.403	0.172
GDP_diabetes_cal$proportion_plant_prot	-2.40e+13	2.44e+13	-0.984	0.334

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	4.81e+12	1.70e+12	2.83	0.009	**
GDP_diabetes_cal$proportion_carbs	-8.14e+12	3.22e+12	-2.53	0.018

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	-2.27e+12	1.15e+12	-1.98	0.058	.
GDP_diabetes_cal$proportion_fat	7.97e+12	3.24e+12	2.46	0.02

With proportions, correlation is higher than with calorie count and linear regression parameters a bit more significant but the relationships are still not strong enough.

-top 5, last 5 ?

4.How do these differences relate to the diabetes prevalence in these countries ? What is the typical diet that can be observed in richer states that relates to lower diabetes prevalence ?

Conclusion

• Take home message
• Limitations
• Future work?